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A Systematic Review of Expert Systems for Improving Energy Efficiency in the Manufacturing Industry
被引:3
作者:
Ioshchikhes, Borys
[1
]
Frank, Michael
[1
]
Weigold, Matthias
[1
]
机构:
[1] Tech Univ Darmstadt, Inst Prod Management Technol & Machine Tools PTW, Otto Berndt Str 2, D-64287 Darmstadt, Germany
来源:
关键词:
sustainability;
climate neutrality;
industrial processes;
energy analysis;
optimization;
NEURAL-NETWORKS;
FUZZY-LOGIC;
PERFORMANCE;
KNOWLEDGE;
SUSTAINABILITY;
OPTIMIZATION;
METHODOLOGY;
CONSUMPTION;
TOOL;
D O I:
10.3390/en17194780
中图分类号:
TE [石油、天然气工业];
TK [能源与动力工程];
学科分类号:
0807 ;
0820 ;
摘要:
Against the backdrop of the European Union's commitment to achieve climate neutrality by 2050, efforts to improve energy efficiency are being intensified. The manufacturing industry is a key focal point of these endeavors due to its high final electrical energy demand while simultaneously facing a growing shortage of skilled workers crucial for meeting established goals. Expert systems (ESs) offer the chance to overcome this challenge by automatically identifying potential energy efficiency improvements, thereby playing a significant role in reducing electricity consumption. This paper systematically reviews state-of-the-art ES approaches aimed at improving energy efficiency in industry with a focus on manufacturing. The literature search yields 1668 results, of which 62 articles published between 1987 and 2024 are analyzed in depth. These publications are classified according to the system boundary, manufacturing type, application perspective, application purpose, ES type, and industry. Furthermore, we examine the structure, implementation, utilization, and development of ESs in this context. Through this analysis, this review reveals research gaps, pointing toward promising topics for future research.
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页数:21
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